A variety of techniques for compressing digital audio or voice signals are known. For example, sub-band coding, a non-block-forming frequency band dividing system, in which the input audio signal is not divided in time into blocks, but is divided in frequency by a filter into plural frequency bands for quantizing. In a block-forming frequency and dividing system, such as a transform coding system, the input audio signal in the time domain is converted into spectral coefficients in the frequency domain by an orthogonal transform. The resulting spectral coefficients are divided into plural frequency bands, and the spectral coefficients in each band are quantized.
There is also known a technique consisting of a combination of sub-band coding and transform coding, in which frequency range signals produced by dividing the input audio signal in frequency are individually orthogonally transformed into spectral coefficients. The spectral coefficients are then divided into plural frequency bands, and the spectral coefficients in each band are then quantized.
Among the filters useful for dividing a digital audio input signal into bands is the quadrature mirror (QMF) filter, which is described, for example, in R. E. Crochiere, Digital Coding of Speech in Sub-bands, 55 Bell. Syst. Tech. J. No. 8, (1976). The technique of dividing the audio input signal in frequency into frequency bands of an equal width is discussed in Joseph H. Rothweiler, Polyphase Quadrature Filers--a New Sub-band Coding Technique, ICASSP 83, BOSTON (1983).
As a technique for quantizing the spectral coefficients obtained by frequency division, a sub-band system which takes the characteristics of the human sense of hearing into account is known. The audio frequency range may be divided in frequency into plural bands, such as 25 critical bands, which have a bandwidth that increases with increasing frequency. The spectral coefficients in each of the respective bands are quantized by adaptive bit allocation applied to each band. For example, the spectral coefficients resulting from a modified discrete cosine transform (MDCT) are divided into bands and the spectral coefficients in each band are quantized using an adaptively-determined number of bits.
Two known adaptive bit allocation techniques will be now be described. First, in the technique described in ASSP-25, IEEE TRANSACTIONS OF ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, No. 4, August 1977, bit allocation is carried out on the basis of the magnitude of the signals of the respective bands. Although this system provides a flat quantizing noise spectrum, and minimizes noise energy, noise perceived by the listener is not minimized because this technique does not exploit the masking characteristics of the human sense of hearing.
On the other hand, the technique described in M. A. Kransner, The Critical Band Coder-Digital Encoding of the Perceptual Requirements of the Auditory System, ICASSP 1980, uses the masking characteristics of the human sense of heating to determine the signal-to-noise ratio necessary for each band to make a fixed quantizing bit allocation. However, this technique provides relatively poor results with a single sine-wave input because of its fixed bit allocation.
Thus, the subjective noise level is not optimum if the bit allocation is made depending on the magnitude of the band signals to minimize the quantizing noise level, whereas satisfactory signal-to-noise characteristics are unlikely to be produced with a fixed bit allocation taking account only of masking.
To overcome the above-mentioned drawbacks in the adaptive bit allocation techniques discussed above, the data compression apparatus described in U.S. patent application Ser. No. 07/924,298, the specification of which is incorporated herein by reference, has been proposed. In this apparatus, the total number of bits available for quantizing all the spectral coefficients resulting from orthogonally transforming a digital input signal is divided between bits to be allocated according to the level of the input signal (level-dependent bits) and bits to be allocated according to the spectral distribution of the input signal (spectrum-dependent bits). A total number of bits, consisting of level-dependent bits and spectrum-dependent bits, is allocated to each band. Each spectral coefficient in the band is quantized with the allocated number of bits. The total number of bits allocated for quantizing each spectral coefficient in each band is the sum of the number of level-dependent bits allocated to the band and the number of spectrum-dependent bits allocated to the band.
The division ratio of the total available number of quantizing bits between level-dependent bits and spectrum-dependent bits can be variable, depending on a signal related to the input signal such that, the smoother the spectrum of the input signal (i.e., the less tonal the input signal), the larger is the division ratio in favor of level-dependent bits. For each block of the digital audio input signal, the number of level-dependent bits allocated for quantizing the each of the spectral coefficients in each band is determined according to one of plural predetermined bit allocation patterns selected according to the level of the input signal. The number of spectrum-dependent bits allocated for quantizing the spectral coefficients in each band corresponding to each block of the digital audio input signal depends on the band magnitude of each band. The band magnitude can be any one of the energy of the band, the peak level in the band, the integrated level over the band, or some other suitable parameter relating to the band.
If the energy of the input signal is concentrated in particular spectral regions, as in the case of a single sine wave input, the quantizing bit allocation technique just described enables the number of bits allocated to bands containing high levels of spectral energy to be increased to improve the overall signal-to-noise characteristics. Since the human sense of hearing is, in general, highly sensitive to signals containing narrow spectral components, the above bit allocation technique improves not only the measured value of signal-to-noise ratio, but also the signal-to-noise ratio perceived by the listener.
However, if spectrum-dependent bit allocation is performed simply with the purpose of improving the signal-to-noise characteristics, a sufficient number of bits cannot be allocated to bands corresponding to the spectral regions in a signal containing a large number of narrow spectral components, such as the sound of a triangle. Accordingly, U.S. patent application Ser. No. 08/011,376, the specification of which is also incorporated herein by reference, discloses a version of the compressor just described in which the number of spectrum-dependent bits allocated for quantizing the spectral coefficients in each band depends on the band magnitude of the band, weighted depending on the band frequency.